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MMRF for proteome annotation applied to human protein disease prediction

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorGarcía Jiménez, Beatriz
dc.contributor.authorLedezma Espino, Agapito Ismael
dc.contributor.authorSanchis de Miguel, María Araceli
dc.date.accessioned2011-07-07T14:22:27Z
dc.date.available2011-07-07T14:22:27Z
dc.date.issued2011
dc.descriptionProceedings of: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010
dc.description.abstractBiological processes where every gene and protein participates is an essential knowledge for designing disease treatments. Nowadays, these annotations are still unknown for many genes and proteins. Since making annotations from in-vivo experiments is costly, computational predictors are needed for different kinds of annotation such as metabolic pathway, interaction network, protein family, tissue, disease and so on. Biological data has an intrinsic relational structure, including genes and proteins, which can be grouped by many criteria. This hinders the possibility of finding good hypotheses when attribute-value representation is used. Hence, we propose the generic Modular Multi-Relational Framework (MMRF) to predict different kinds of gene and protein annotation using Relational Data Mining (RDM). The specific MMRF application to annotate human protein with diseases verifies that group knowledge (mainly protein-protein interaction pairs) improves the prediction, particularly doubling the area under the precision-recall curve
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationInductive logic programming: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Berlin: Springer, 2011, p. 67-75 (Lecture notes in computer science. Lecture notes in artificial intelligence; 6489) ISBN 978-3-642-21295-6
dc.identifier.doi10.1007/978-3-642-21295-6
dc.identifier.isbn978-3-642-21294-9 (Print)
dc.identifier.isbn978-3-642-21295-6 (Online)
dc.identifier.issn0302-9743 (Print)
dc.identifier.issn1611-3349 (Online)
dc.identifier.publicationfirstpage67
dc.identifier.publicationlastpage75
dc.identifier.publicationtitleInductive Logic Programming: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers
dc.identifier.urihttps://hdl.handle.net/10016/11716
dc.language.isoeng
dc.publisherSpringer
dc.relation.eventdateJune 2010
dc.relation.eventnumber20
dc.relation.eventplaceFlorence, Italy
dc.relation.eventtitleInternational Conference, ILP 2010
dc.relation.ispartofseriesLecture notes in computer science. Lecture notes in artificial intelligence
dc.relation.ispartofseries6489
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-642-21295-6
dc.rights© Springer
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherRelational data mining
dc.subject.otherHuman disease annotation
dc.subject.otherMulti-class relational decision tree
dc.subject.otherFirst-order logic
dc.subject.otherStructured data
dc.titleMMRF for proteome annotation applied to human protein disease prediction
dc.typeconference poster*
dc.type.hasVersionAM*
dspace.entity.typePublication
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